66 lines
1.7 KiB
Matlab
66 lines
1.7 KiB
Matlab
%%%%%%%%%%%%%%%%%%%%%
|
|
% Script task: Normalize RGB data and plot FFT using the power spectra
|
|
%
|
|
% Input : RGB_data.csv -> average RGB values of each image
|
|
%
|
|
% Output : Fast Fourier Transform of X(t): a graph representing the Single-Sided Amplitude Spectrum of X(t)
|
|
%
|
|
% Author: Loic Delattre and Maryne DEY (maryne.dey@ecam.fr, loic.delattre@ecam.fr)
|
|
% Date: 07/02/2023
|
|
%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
clear all
|
|
close all
|
|
clc
|
|
|
|
%To be able to extract from external format (excel)
|
|
pkg load io
|
|
|
|
%Normalization of the data
|
|
data = frames_RGBs ()';
|
|
standard_deviation = std(data);
|
|
mean_value = mean(data);
|
|
|
|
for i = 1:size(data,1)
|
|
normalized_data_G(i,1) = (data(i,2)-mean_value(2))/standard_deviation(2); %%2 and not 1 because green is the 2nd color
|
|
endfor
|
|
|
|
%Input characteristics
|
|
img_num = length(data);
|
|
Fs = img_num/32; % Sampling frequency = X images in 32 seconds
|
|
T = 1/Fs; % Sampling period
|
|
L = img_num; % Length of signal = 32 seconds
|
|
t = (0:L-1)*T; % Time vector
|
|
|
|
X = normalized_data_G;
|
|
|
|
%Plot of the RGB data in the time domain
|
|
plot(t(1:L),X(1:L))
|
|
title("Signal")
|
|
xlabel("t (milliseconds)")
|
|
ylabel("X(t)")
|
|
|
|
%Finding power spectra P2 and P1
|
|
Y = fft(X);
|
|
P2 = abs(Y/L);
|
|
P1 = P2(1:L/2+1);
|
|
P1(2:end-1) = 2*P1(2:end-1);
|
|
f = Fs*(0:(L/2))/L;
|
|
|
|
% Finding boudaries
|
|
start = find(f==0.75);
|
|
stop = find(f==4);
|
|
P1_graph = P1(start:stop);
|
|
f_graph = f(start:stop);
|
|
|
|
%Graph of FFT in function of the frequency
|
|
plot(f_graph,P1_graph)
|
|
title("Single-Sided Amplitude Spectrum of X(t)")
|
|
xlabel("f (Hz)")
|
|
ylabel("|P1(f)|")
|
|
|
|
%Extracting the heart rate
|
|
[heart_rate_Hz, index] = max(P1_graph);
|
|
corresponding_frequency = f_graph(index);
|
|
heart_rate_bpm = corresponding_frequency*60;
|
|
fprintf("Your heart rate is about %d bpm.\n", heart_rate_bpm) |